Guest Editorial

Modeling and analyzing networks is a major emerging topic in different research areas, such as computational biology, social science, document retrieval and social web applications. By connecting objects, it is possible to obtain an intuitive and global view of the relationships among components of a complex system. Nowadays, scientific communities have access to huge volume of network-structured data, such as social networks, gene/proteins/metabolic networks, sensor networks, and peer-to-peer networks. Often, data is collected at different time points allowing capturing a dynamic trend of the observed network. Consequently, the time component plays a key role in the comprehension of the evolutionary behavior of the studied network (evolution of the network structure and/or of flows within the system). Time can help to determine the real causal relationships within, for instance, gene activations, link creation, and information flow. Handling such data is a major challenge for current research in machine learning and data mining, and it has led to the development of recent innovative techniques that consider complex/multi-level networks, time-evolving graphs, heterogeneous information (nodes and links), and requires scalable algorithms that are able to manage large-scale complex networks. This special issue is the follow-up of the Dynamic Networks and Knowledge Discovery workshop (DyNaK)1 that has been held in conjunction to ECML-PKDD 2011 at Barcelona on September 24th 2011. The workshop was motivated by the interest of providing a meeting point for scientists with different backgrounds who are interested in the study of large-scale dynamic complex networks. The workshop has attracted 18 submissions out of which 9 papers has been accepted. The workshop has gathered more than 30 participants and was also the host of three highly appreciated invited keynotes and one industrial talk. Building on the success of the DyNaK workshop, an open call for papers has been issued for this special issue, focusing on the major topic discussed in the workshop: analyzing, modeling and mining large-scale real network. 15 high quality papers have been received; each of which has been reviewed by three reviewers. Only 7 contributions were finally selected. These contributions show the vitality of the field: a broad panel of techniques are applied to modeling the dynamics of complex systems, using a wide set of formalisms ranging from descriptive rules to Probabilistic Real-Time Automata. Application fields are also wide: vision, opinion diffusion in social network, business process modeling and text mining. In Internal link prediction: a new approach for predicting links in bipartite graphs, Allali et al. present an algorithm for predicting internal link in bipartite graph. They address the problem of predicting